{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:EQBE6E4NRJ7YROVLDFNETBGXMG","short_pith_number":"pith:EQBE6E4N","canonical_record":{"source":{"id":"1809.02010","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-06T14:28:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2aad5009fdd853ee7d51d4b5faceb8bfdd5a96211c5fd4b1ddf584f8fbe3be8e","abstract_canon_sha256":"cbcbe22954636a7fe8a21a32518b7e061b6afd4f3090a327002d1e9df6495b07"},"schema_version":"1.0"},"canonical_sha256":"24024f138d8a7f88baab195a4984d761b65ce561583efee63dc61f517d38d49d","source":{"kind":"arxiv","id":"1809.02010","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.02010","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"arxiv_version","alias_value":"1809.02010v2","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02010","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"pith_short_12","alias_value":"EQBE6E4NRJ7Y","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EQBE6E4NRJ7YROVL","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EQBE6E4N","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:EQBE6E4NRJ7YROVLDFNETBGXMG","target":"record","payload":{"canonical_record":{"source":{"id":"1809.02010","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-06T14:28:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2aad5009fdd853ee7d51d4b5faceb8bfdd5a96211c5fd4b1ddf584f8fbe3be8e","abstract_canon_sha256":"cbcbe22954636a7fe8a21a32518b7e061b6afd4f3090a327002d1e9df6495b07"},"schema_version":"1.0"},"canonical_sha256":"24024f138d8a7f88baab195a4984d761b65ce561583efee63dc61f517d38d49d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:52.859343Z","signature_b64":"XpI5CwwalNLMki/G2ebEmIMGjjTp+08DZQk/d206HYUlsnbtqdSTuGov/x48iJq6kwCBc1Dcp5kFEE3At3IADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24024f138d8a7f88baab195a4984d761b65ce561583efee63dc61f517d38d49d","last_reissued_at":"2026-05-17T23:45:52.858896Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:52.858896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.02010","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:45:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MOEa/3fmBKHnWSAyr3DyxyOlp7i+2p07Cby41GUPkc/20M+gnaH0gYmwI2QFQL2rfiKzqcpE3kNUlqe/sB2bDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:17:04.327416Z"},"content_sha256":"9d2091c056b1cbdc31f0825b0e1320777cfbf9bb1283a9d171003be8aa169ed4","schema_version":"1.0","event_id":"sha256:9d2091c056b1cbdc31f0825b0e1320777cfbf9bb1283a9d171003be8aa169ed4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:EQBE6E4NRJ7YROVLDFNETBGXMG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Gaussian Process Regression for Binned Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Mauricio A Alvarez, Michael Thomas Smith, Neil D Lawrence","submitted_at":"2018-09-06T14:28:26Z","abstract_excerpt":"Many datasets are in the form of tables of binned data. Performing regression on these data usually involves either reading off bin heights, ignoring data from neighbouring bins or interpolating between bins thus over or underestimating the true bin integrals.\n  In this paper we propose an elegant method for performing Gaussian Process (GP) regression given such binned data, allowing one to make probabilistic predictions of the latent function which produced the binned data.\n  We look at several applications. First, for differentially private regression; second, to make predictions over other "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02010","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:45:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aodWbJf6iWZsjg29mjFPznONrQ+nzmxG0UWkqw1nG6QNtdiqrR84mvY4AdbQmWkjTS9052EmIht0K9z17ZVPBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:17:04.327766Z"},"content_sha256":"ba7ce00b32d525206e3df64e0530ca6fa8ff51f693d0adf9d6f5277b90c1e568","schema_version":"1.0","event_id":"sha256:ba7ce00b32d525206e3df64e0530ca6fa8ff51f693d0adf9d6f5277b90c1e568"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EQBE6E4NRJ7YROVLDFNETBGXMG/bundle.json","state_url":"https://pith.science/pith/EQBE6E4NRJ7YROVLDFNETBGXMG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EQBE6E4NRJ7YROVLDFNETBGXMG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T14:17:04Z","links":{"resolver":"https://pith.science/pith/EQBE6E4NRJ7YROVLDFNETBGXMG","bundle":"https://pith.science/pith/EQBE6E4NRJ7YROVLDFNETBGXMG/bundle.json","state":"https://pith.science/pith/EQBE6E4NRJ7YROVLDFNETBGXMG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EQBE6E4NRJ7YROVLDFNETBGXMG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:EQBE6E4NRJ7YROVLDFNETBGXMG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"cbcbe22954636a7fe8a21a32518b7e061b6afd4f3090a327002d1e9df6495b07","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-06T14:28:26Z","title_canon_sha256":"2aad5009fdd853ee7d51d4b5faceb8bfdd5a96211c5fd4b1ddf584f8fbe3be8e"},"schema_version":"1.0","source":{"id":"1809.02010","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.02010","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"arxiv_version","alias_value":"1809.02010v2","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02010","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"pith_short_12","alias_value":"EQBE6E4NRJ7Y","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EQBE6E4NRJ7YROVL","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EQBE6E4N","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:ba7ce00b32d525206e3df64e0530ca6fa8ff51f693d0adf9d6f5277b90c1e568","target":"graph","created_at":"2026-05-17T23:45:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Many datasets are in the form of tables of binned data. Performing regression on these data usually involves either reading off bin heights, ignoring data from neighbouring bins or interpolating between bins thus over or underestimating the true bin integrals.\n  In this paper we propose an elegant method for performing Gaussian Process (GP) regression given such binned data, allowing one to make probabilistic predictions of the latent function which produced the binned data.\n  We look at several applications. First, for differentially private regression; second, to make predictions over other ","authors_text":"Mauricio A Alvarez, Michael Thomas Smith, Neil D Lawrence","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-06T14:28:26Z","title":"Gaussian Process Regression for Binned Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02010","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9d2091c056b1cbdc31f0825b0e1320777cfbf9bb1283a9d171003be8aa169ed4","target":"record","created_at":"2026-05-17T23:45:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"cbcbe22954636a7fe8a21a32518b7e061b6afd4f3090a327002d1e9df6495b07","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-06T14:28:26Z","title_canon_sha256":"2aad5009fdd853ee7d51d4b5faceb8bfdd5a96211c5fd4b1ddf584f8fbe3be8e"},"schema_version":"1.0","source":{"id":"1809.02010","kind":"arxiv","version":2}},"canonical_sha256":"24024f138d8a7f88baab195a4984d761b65ce561583efee63dc61f517d38d49d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24024f138d8a7f88baab195a4984d761b65ce561583efee63dc61f517d38d49d","first_computed_at":"2026-05-17T23:45:52.858896Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:45:52.858896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XpI5CwwalNLMki/G2ebEmIMGjjTp+08DZQk/d206HYUlsnbtqdSTuGov/x48iJq6kwCBc1Dcp5kFEE3At3IADQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:45:52.859343Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.02010","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d2091c056b1cbdc31f0825b0e1320777cfbf9bb1283a9d171003be8aa169ed4","sha256:ba7ce00b32d525206e3df64e0530ca6fa8ff51f693d0adf9d6f5277b90c1e568"],"state_sha256":"837c472ef05854c3364cbc2a48ebb27d8e7d94cce4ba19e4e03521781e43f812"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yy8TZ6w0FmCoc8nJhPA8YkMyL34sHoVYGg92kQi9KsrVU5D73SphcJnV12z7VSbw2m4uqmSZwccyavHVmjngAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T14:17:04.329737Z","bundle_sha256":"2769c06676f231a414afacf7c7ae0da96d37cf063a91b4498e3ed6b3ae64b4f0"}}